Predictive Gene Signatures Determine Tumor Sensitivity to MDM2 Inhibition.
Predictive Gene Signatures Determine Tumor Sensitivity to MDM2 Inhibition.
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DOI：
10.1158/0008-5472.can-17-0949
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发表时间：
2018-05-15
期刊：
影响因子：
11.2
通讯作者：
Andreeff M
中科院分区：
文献类型：
--
作者：
Ishizawa J;Nakamaru K;Seki T;Tazaki K;Kojima K;Chachad D;Zhao R;Heese L;Ma W;Ma MCJ;DiNardo C;Pierce S;Patel KP;Tse A;Davis RE;Rao A;Andreeff M
使用鼠双微体2（MDM 2）抑制剂的早期临床试验证明了在癌细胞中通过MDM 2抑制p53诱导的细胞凋亡的概念验证，然而，并非所有野生型TP 53肿瘤对MDM 2抑制敏感。因此，需要更有效的抑制剂和预测肿瘤敏感性的生物标志物。新型MDM 2抑制剂DS-3032 b的效力是第一代抑制剂nutlin-3a的10倍。TP 53突变可预测DS-3032 b耐药，TP 53突变的等位基因频率与DS-3032 b敏感性呈负相关。然而，TP 53野生型肿瘤对DS-3032 b的敏感性差异很大。因此，我们使用两种方法来创建预测性基因签名：首先，通过比较对MDM 2抑制的敏感性与240种癌细胞系中的基础mRNA表达谱，在患者来源的肿瘤异种移植模型和离体人急性髓性白血病（AML）细胞中定义并验证了175个基因签名。其次，AML特异性1532基因签名通过使用41个原发性AML样品的基因表达谱进行随机森林分析和交叉验证来定义。TP 53突变状态与两种基因标签的组合提供了最佳的阳性预测值（81%和82%，相比之下，单独的TP 53突变状态为62%）。此外，从AML特异性1532-基因签名中选择的排名最高的50个基因保留了高预测性能，表明通过该方法可以产生用于临床实施的更可行的基因签名大小。我们的模型正在进行的MDM 2抑制剂临床试验中进行测试。
Early clinical trials using murine double minute 2 (MDM2) inhibitors demonstrated proof-of-concept of p53-induced apoptosis by MDM2 inhibition in cancer cells, however, not all wild-type TP53 tumors are sensitive to MDM2 inhibition. Therefore, more potent inhibitors and biomarkers predictive of tumor sensitivity are needed. The novel MDM2 inhibitor DS-3032b is 10-fold more potent than the first-generation inhibitor nutlin-3a. TP53 mutations were predictive of resistance to DS-3032b, and allele frequencies of TP53 mutations were negatively correlated with sensitivity to DS-3032b. However, sensitivity to DS-3032b of TP53 wild-type tumors varied greatly. We thus used two methods to create predictive gene signatures: First, by comparing sensitivity to MDM2 inhibition with basal mRNA expression profiles in 240 cancer cell lines, a 175-gene signature was defined and validated in patient-derived tumor xenograft models and ex vivo human acute myeloid leukemia (AML) cells. Second, an AML-specific 1532-gene signature was defined by performing random forest analysis with cross validation using gene expression profiles of 41 primary AML samples. The combination of TP53 mutation status with the two gene signatures provided the best positive predictive values (81 and 82%, compared to 62% for TP53 mutation status alone). In addition, the top-ranked 50 genes selected from the AML-specific 1532-gene signature conserved high predictive performance, suggesting that a more feasible size of gene signature can be generated through this method for clinical implementation. Our model is being tested in ongoing clinical trials of MDM2 inhibitors.